Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Mohammad Valipour
  • Sayed M. Bateni
  • Jörg Dietrich
  • Essam Heggy
  • Mansour Almazroui

External Research Organisations

  • Metropolitan State University of Denver (MSU)
  • University of Hawaiʻi at Mānoa
  • University of Southern California
  • California Institute of Caltech (Caltech)
  • King Abdulaziz University
  • University of East Anglia
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Details

Original languageEnglish
Pages (from-to)4639-4665
Number of pages27
JournalInternational Journal of Remote Sensing
Volume44
Issue number15
Early online date28 Jul 2023
Publication statusE-pub ahead of print - 28 Jul 2023

Abstract

Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2’ climate reanalysis and ESA’ Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season.

Keywords

    climate reanalysis, combining datasets, CONUS, measurement, NASA, remote sensing retrievals, SCAN, Soil moisture

ASJC Scopus subject areas

Sustainable Development Goals

Cite this

Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States. / Valipour, Mohammad; Bateni, Sayed M.; Dietrich, Jörg et al.
In: International Journal of Remote Sensing, Vol. 44, No. 15, 28.07.2023, p. 4639-4665.

Research output: Contribution to journalArticleResearchpeer review

Valipour M, Bateni SM, Dietrich J, Heggy E, Almazroui M. Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States. International Journal of Remote Sensing. 2023 Jul 28;44(15):4639-4665. Epub 2023 Jul 28. doi: 10.1080/01431161.2023.2237665
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title = "Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States",
abstract = "Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2{\textquoteright} climate reanalysis and ESA{\textquoteright} Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season.",
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note = "Funding Information: Part of this research is funded under support from the Zumberge Research and Innovation Fund of the University of Southern California allocated to the Arid Climates and Water Research Center—AWARE. The authors tend to have special thanks to Dr. John Nieber, Professor at the University of Minnesota for his invaluable and constructive comments on the paper.",
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T1 - Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States

AU - Valipour, Mohammad

AU - Bateni, Sayed M.

AU - Dietrich, Jörg

AU - Heggy, Essam

AU - Almazroui, Mansour

N1 - Funding Information: Part of this research is funded under support from the Zumberge Research and Innovation Fund of the University of Southern California allocated to the Arid Climates and Water Research Center—AWARE. The authors tend to have special thanks to Dr. John Nieber, Professor at the University of Minnesota for his invaluable and constructive comments on the paper.

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